code: def test_use_selected_layers_of_bert_for_different_purposes(self):
      class MediumTransformer(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.embeddings = (
                  TransformerEmbeddings.from_pretrained_module(
                      "bert-base-cased",
                      relevant_module="bert.embeddings",
                  )
              )
              self.separate_transformer = (
                  TransformerStack.from_pretrained_module(
                      "bert-base-cased",
                      relevant_module="bert.encoder",
                      num_hidden_layers=8,
                      strict=False,
                  )
              )
              self.combined_transformer = (
                  TransformerStack.from_pretrained_module(
                      "bert-base-cased",
                      relevant_module="bert.encoder",
                      num_hidden_layers=4,
                      mapping={
                          f"layer.{l}": f"layers.{i}"
                          for (i, l) in enumerate(range(8, 12))
                      },
                      strict=False,
                  )
              )
  
          def forward(
              self,
              left_token_ids: torch.LongTensor,
              right_token_ids: torch.LongTensor,
          ):
              left = self.embeddings(left_token_ids)
              left = self.separate_transformer(left)
  
              right = self.embeddings(right_token_ids)
              right = self.separate_transformer(right)
  
              # combine the sequences in some meaningful way. here, we just add them.
              # combined = combine_masked_sequences(left, left_mask, right, right_mask)
              combined = left + right
  
              return self.combined_transformer(combined)
  
      medium = MediumTransformer()
      assert (len(medium.separate_transformer.layers)) == 8
      assert (len(medium.combined_transformer.layers)) == 4
  
      pretrained = cached_transformers.get("bert-base-cased", False)
      pretrained_layers = dict(pretrained.encoder.layer.named_modules())
  
      separate_layers = dict(
          medium.separate_transformer.layers.named_modules()
      )
      assert_allclose(
          separate_layers["0"].intermediate.dense.weight.data,
          pretrained_layers["0"].intermediate.dense.weight.data,
      )
  
      combined_layers = dict(
          medium.combined_transformer.layers.named_modules()
      )
      assert_allclose(
          combined_layers["0"].intermediate.dense.weight.data,
          pretrained_layers["8"].intermediate.dense.weight.data,
      )
      assert_allclose(
          combined_layers["1"].intermediate.dense.weight.data,
          pretrained_layers["9"].intermediate.dense.weight.data,
      )
      assert_allclose(
          combined_layers["2"].intermediate.dense.weight.data,
          pretrained_layers["10"].intermediate.dense.weight.data,
      )
      assert_allclose(
          combined_layers["3"].intermediate.dense.weight.data,
          pretrained_layers["11"].intermediate.dense.weight.data,
      )
  
file_path: allennlp/tests/modules/transformer/toolkit_test.py
imports: import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import cached_transformers
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.modules.token_embedders import Embedding, TokenEmbedder
  from allennlp.modules.transformer import TransformerStack, TransformerEmbeddings, TransformerPooler
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 4 for calling the TestTransformerToolkit obj's underlying member method code with example usage: obj.test_use_selected_layers_of_bert_for_different_purposes() and returns: self